STeP: Scalable Tenant Placement for Managing Database-as-a-Service Deployments
STeP: Scalable Tenant Placement for Managing Database-as-a-Service Deployments
复制标题
STeP：用于管理数据库即服务部署的可扩展租户放置
DOI：
10.1145/2987550.2987575
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
D. DeWitt
中科院分区：
文献类型：
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作者：
Rebecca Taft;Willis Lang;Jennie Duggan;Aaron J. Elmore;M. Stonebraker;D. DeWitt
具有数据库的公共云提供商必须有效地将计算资源分配给每个客户。有效的租户分配既减少了使用的物理服务器的数量，并在云市场竞争激烈的价格上达到客户期望。对于像Microsoft和Amazon这样的公共云供应商，这意味着将数百万用户的数据库包装到数百或数千台服务器上。本文通过检查了三个月的时间里从微软的Azure SQL数据库生产系统中检查公开发布的匿名客户资源使用统计数据集，研究租户的安置。我们实施了步骤框架来摄入和分析这个大数据集。步骤使我们可以使用此生产数据集评估几种将数据库租户包装到服务器上的新算法。这些技术通过将租户与兼容的资源使用模式相交，从而产生高效的包装。评估表明，在生产的客户工作量下，这些技术对节点数量的变化非常有力，从而使性能违规行为违反，甚至对于高密度租户包装。与数据收集时生产中使用的算法相比，我们的算法违反了性能违规行为的90％，并节省了云提供商总运营成本的32％。
Public cloud providers with Database-as-a-Service offerings must efficiently allocate computing resources to each of their customers. An effective assignment of tenants both reduces the number of physical servers in use and meets customer expectations at a price point that is competitive in the cloud market. For public cloud vendors like Microsoft and Amazon, this means packing millions of users' databases onto hundreds or thousands of servers. This paper studies tenant placement by examining a publicly released dataset of anonymized customer resource usage statistics from Microsoft's Azure SQL Database production system over a three-month period. We implemented the STeP framework to ingest and analyze this large dataset. STeP allowed us to use this production dataset to evaluate several new algorithms for packing database tenants onto servers. These techniques produce highly efficient packings by collocating tenants with compatible resource usage patterns. The evaluation shows that under a production-sourced customer workload, these techniques are robust to variations in the number of nodes, keeping performance objective violations to a minimum even for high-density tenant packings. In comparison to the algorithm used in production at the time of data collection, our algorithms produce up to 90% fewer performance objective violations and save up to 32% of total operational costs for the cloud provider.